Unsupervised Labor Intelligence Systems: A Detection Approach and Its Evaluation: A Case Study in the Netherlands

Abstract

In recent years, job advertisements through the web or social media represent an easy way to spread this information. However, social media are often a dangerous showcase of possibly labor exploitation advertisements. This paper aims to determine the potential indicators of labor exploitation for unskilled jobs offered in the Netherlands. Specifically, we exploited topic modeling to extract and handle information from textual data about job advertisements for analyzing deceptive and characterizing features. Finally, we use these features to investigate whether automated machine learning methods can predict the risk of labor exploitation by looking at salary discrepancies. The results suggest that features need to be carefully monitored, e.g., hours. Finally, our results showed encouraging results, i.e., F1-Score 61%, thus meaning that Data Science methods and Artificial Intelligence approaches can be used to detect labor exploitation—starting from job advertisements—based on the discrepancy of delta salary, possibly representing a revolutionary step.